Veritone Alpha, Inc.

Michael Luis Sandoval

Wolf Kohn

Jonathan Cross

Vishnu Vettrivel

David Talby

Jason Knox

Mike Lazarus

David Kettler

Brian Schaper

Neel Master

Brandon Weiss

Pengbo Zhang

Techniques are described for implementing automated control systems that manipulate operations of specified target systems, such as by modifying or otherwise manipulating inputs or other control elements of the target system that affect its operation (e.g., affect output of the target system). An automated control system may in some situations have a distributed architecture with multiple decision modules that each controls a portion of a target system and operate in a partially decoupled manner with respect to each other, such as by each decision module operating to synchronize its local solutions and proposed control actions with those of one or more other decision modules, in order to determine a consensus with those other decision modules. Such inter-module synchronizations may occur repeatedly to determine one or more control actions for each decision module at a particular time, as well as to be repeated over multiple times for ongoing control.

Determining control actions of decision modules

Techniques are described for implementing automated control systems that manipulate operations of specified target systems, such as by modifying or otherwise manipulating inputs or other control elements of the target system that affect its operation (e.g., affect output of the target system). An automated control system may in some situations have a distributed architecture with multiple decision modules that each controls a portion of a target system, and may further have one or more components that interacts with one or more users to obtain a description of the target system, including restrictions related to the various elements of the target system, and one or more goals to be achieved during control of the target system. The component(s) then perform various automated actions to generate, test and deploy one or more executable decision modules to use in performing the control of the target system based on the user-specified information.

Cooperative distributed control of target systems

Techniques are described for implementing automated control systems to control operations of specified physical target systems, such as with one or more batteries used to store and provide electrical power. Characteristics of each battery's state may be used to perform automated control of DC power from the battery, such as in a real-time manner and to optimize long-term operation of the battery. In some situations, multiple batteries are controlled by using multiple control systems each associated with one of the batteries, and with overall control being coordinated in a distributed manner using interactions between the multiple control systems. A system that includes one or more batteries to be controlled may further include additional components in some situations, such as one or more electrical sources and/or one or more electrical loads, with one non-exclusive example of a type of such system being one or more home electrical power systems.

Using battery DC characteristics to control power output

Techniques are described for implementing automated control systems to control operations of specified physical target systems. In some situations, the described techniques include forecasting future values of parameters that affect operation of a target system, and using the forecasted future values as part of determining current automated control actions to take for the target system&#x2014;in this manner, the current automated control actions may be improved relative to other possible actions that do not reflect such forecasted future values. Various automated operations may also be performed to improve the forecasting in at least some situations, such as by combining the use of multiple different types of forecasting models and multiple different groups of past data to use for training the models, and/or by improving the estimated internal non-observable state information reflected in at least some of the models.

Using forecasting to control target systems

Techniques are described for implementing automated control systems to control operations of specified physical target systems. In some situations, the described techniques include obtaining and analyzing sensor data about operations of a target system in order to generate an improved model of a current state of the target system, and using the modeled state information as part of determining further current and/or future automated control actions to take for the target system, such as to generate a function and/or other structure that models internal operations of the target system, rather than merely attempting to estimate target system output from input without understanding the internal structure and operations of the target system.

Using sensor data to assist in controlling a target system by modeling the functionality of the target system

Techniques are described for reducing or dampening harmonics in a power signal to be supplied to a power system. The techniques may also be used to synchronize the power signal to be supplied to the power system with the power that is currently present on the power system. The techniques operate to step down the signals to be processed, process the signals using low-current op amps, and then step the signals back up to be transmitted on a high current system. The values of the circuit components may be determined by using a solution for an accompanying transfer function.

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Bellevue, WA, United States of America

Michael Luis Sandoval